Observed Vertical Dispersion Patterns of Particulate Matter in Urban Street Canyons and Dominant Influencing Factors
Bibliographic record
Abstract
When developing strategies aimed at mitigating air pollution in densely populated urban areas, it is vital to accurately investigate the vertical distribution of airborne particulate matter (PM) and its primary influencing factors. For this study, field experiments were conducted to quantify the vertical distribution and dispersion processes of PM at five vertical heights related to trees—including at street level near vehicular emission sources (0.3 m), pedestrian breathing height (1.5 m), beneath the canopy (6 m), mid-canopy (9 m), and the top of the canopy (12 m)—within a street-facing building in Wuhan, China. Comparing the vertical dispersion patterns of PM with six particle sizes (PM1, PM2.5, PM4, PM7, PM10, and total suspended particulates—TSPs), larger particles exhibited more pronounced variations with height, notably TSPs (correlation coefficient of −0.95) and PM10 (−0.84). The findings consistently revealed a downward trend in PM concentrations across various particle sizes with increasing height, indicating a negative linear correlation between particle concentrations and altitude within the street canyon. For every 1% increase in vertical height, the PM2.5 concentration decreased by approximately 5.44%, the PM10 concentration decreased by 132.1%, and the TSP concentration decreased by 180.6%. These findings show potential for guiding building designers in developing effective strategies, such as optimal vent placement, in order to mitigate the intrusion of outdoor air pollution—particularly PM2.5—into indoor environments. Furthermore, this research provides novel insights for residents living in street-facing buildings and individuals with respiratory diseases, aiding them in the selection of residential floors to minimize health risks associated with exposure to respirable PM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".